A fantasy sports game is a simulation game where users act as managers or owners of simulated sport teams called “fantasy teams,” where each team comprises a number of “players.” Thus, the term “owner” is used to refer to a participant in the fantasy sports game. An owner may be a natural person or a computer-controlled opponent. A “user” is a fantasy owner who is also a natural person. Thus, the term “user” and “owner” are used interchangeably in their roles in the fantasy sports game. In contrast, the term “player” refers to one of the selectable fantasy characters. In certain fantasy sports games, each player corresponds to an athlete in a professional sports league.
Features for conventional fantasy sports games are already known in the art. In a first example, a player evaluation system uses historical data to predict player performance through the end of the season using a blending function. The system is also applied in a draft context by assigning average performance values to the slots on the owner's team that have not yet been filled with players yet to be drafted. In calculating team points, one version weighs certain statistics more heavily than others.
In another conventional feature for a fantasy sports game, a system uses draft position information to make draft recommendations. The draft position information is used to determine the likelihood that a given player, or a class of players, will be available. The draft position information may be used to provide recommendations and information predictive of the available player pool at the time of a given user-participant's draft positions, or at the time of other draft positions. Two common methodologies used for this type of system is Value Based Drafting (“VBD”) or Dynamic VBD (“DVBD”). A player valuation module uses dynamic player valuation, including a durability parameter, a consistency parameter, and a strength of opponents' defenses parameter.
Conventional recommendation engines are also known in the art. An example of such an engine utilizes historical information about competitors' past picks and performance in a fantasy league to determine the methodology the competitor is using to make picks, specifically attempting to match past picks with certain experts to determine which expert's advice the competitor is most likely following. In another conventional recommendation engine, the players on the current roster are compared to the best available players to make a recommendation. In yet another conventional recommendation engine, player analyzing software queries a sports statistics system to analyze the relevant players and delivers the analysis to a roster move recommending software component that delivers to the user roster move recommendations based on the results of the player analysis. The player analysis may be based on actual statistics or projected statistics.
Other recommendation engines relate to other features of a fantasy sports game, such as start/sit recommendations, which tend to be based upon a specific expectation of the points scored and are made without reference to the other team in the matchup. That is, they focus on attempting to score the most possible points without any concern about what the other team will do or how likely victory may be.
Fantasy team owners may consult an average draft position chart, which is information that is publicly available, in order to determine whether or not it is safe to wait one more round in order to make their selection or if they would be well-advised to select the player immediately. Most conventional solutions start with a survey of the available players and then a (frequently arbitrary) determination of their differences and similarities. However, this is a fairly manual process; at best, fans can use a “cheat sheet” assembled by a professional research staff (e.g., a news service or content provider) to reduce the burden.
Some fantasy sports owners, when participating in an auction-style draft, will compute players' auction values before the draft and then approximate the necessary adjustments as the draft goes along.
Successful fantasy owners tend to attempt to ascertain what players other owners in their draft are likely to target, and then adjust their draft strategy accordingly. For example, a team that is drafting 9th in a 10-team league will typically also have the 12th pick. With his 9th pick, he is likely to select the player he believes will be most likely to be selected by the team with the 10th and 11th picks, hoping that another player that he covets more than his opponent may still be available at #12. This is particularly important later in the draft. For example, if a team in a fantasy American football league has the 79th and 82nd selections and needs a quarterback, but the team with the 80th and 81st selections already has an elite quarterback on its roster, it is likely advantageous to select a different position for pick number 79, knowing that the team with picks 80 and 81 is unlikely to select a second quarterback.
Fantasy owners in leagues where teams are matched up against a single opponent (“head-to-head” leagues) are used to attempting to pick up players who have additional games during a matchup, or, in fantasy baseball, players who are “two-start” pitchers (starting pitchers that will get two starts during a week; most only get one, so a two-start pitcher is very valuable).
Successful fantasy team owners are aware of various concepts and make use of them when drafting their fantasy teams. For example, with football, it is usually easy to determine whom the principal backup is for a given running back, and charts that include the teams' bye weeks are readily available online.
One conventional scoring system for fantasy sports is “rotisserie” scoring. In this system, the players' object is to accumulate statistics across a predetermined plurality of sports statistics. During the draft, one goal is to build a team that is expected to meet statistical levels that have generated a victory in the past, in the expectation that aggregate statistical levels during the upcoming season will be approximately equal. Fans who are in a league that carries over from year-to-year will sometimes look at last year's standings to approximate this value, but that is of little use to people who are new to the league or who do not have a representative league available to use for comparison. Thus, there is a need for a recommendation engine to provide weighted recommendations in a fantasy sports game environment.
FIG. 3 shows a method 300 for executing a fantasy sports application to draft players as is known in the art. Specifically, the method 300 relates to a fantasy sports application, executed upon a host, which performs each round of the draft, enabling users to select players in a sequential manner; in one embodiment, this may be a conventional “serpentine” manner. In this embodiment, a user who selects first in an odd number round subsequently selects last in an even number round. In step 310, the draft is initialized. In step 320, an owner or user selects a player and the host executing the fantasy sports application receives the selection, for example, through a network via a transceiver. In step 330, a determination is made whether there are further rounds for selections to be made. If the determination in step 330 indicates that more rounds are to be performed, the method 300 returns to step 320 where further selections are received. If the determination in step 330 indicates that no more rounds are to be performed, the method 300 ends.
FIG. 4 shows a method 400 for executing a fantasy sports application to draft players as is known in the art. Specifically, the method 400 relates to a fantasy sports application, executed upon a host, for users to select players in a conventional auction format. Thus, a user is provided a predetermined budget in which to “bid” for a player and the player who provides the highest “bid” receives the player for the owner's team. In step 410, the draft is initialized. In step 420, an owner or user selects a player and the host executing the fantasy sports application receives the selection, for example, through the network via the transceiver. In step 430, the owner or user provides a bid on the player and the host receives the bid.
In step 430, further owners or users who are also interested in drafting the selected player provide bids and the host receives the respective bids. Thus, in step 440, the host determines the user who provided the highest bid, and that user drafts the player. In step 450, a determination is made whether there are empty slots for a respective position related to the sport in the fantasy sports application. If the determination in step 450 indicates that more auctions are to be performed since there are still empty slots, the method 400 returns to step 420 where further selections are received. If the determination in step 450 indicates that no more slots are empty, the method 400 ends.
When using systems implementing the prior art methods discussed above, fantasy team owners may review player rankings from various sources such as journalists, experts, and historical data, or create their own rankings based upon a synthesis thereof, to assist them in their decision making. Prior art systems, however, do not automatically or adaptively synthesize various weighted ranking sources into a player recommendation.